// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli    Codeplay Software Ltd.
// Ralph Potter  Codeplay Software Ltd.
// Luke Iwanski  Codeplay Software Ltd.
// Contact: <eigen@codeplay.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX

#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL

#include "main.h"
#include <iostream>
#include <stdint.h>
#include <unsupported/Eigen/CXX11/Tensor>

template<typename DataType, int DataLayout, typename IndexType>
void
test_device_memory(const Eigen::SyclDevice& sycl_device)
{
	std::cout << "Running on : "
			  << sycl_device.sycl_queue().get_device().template get_info<cl::sycl::info::device::name>() << std::endl;
	IndexType sizeDim1 = 100;
	array<IndexType, 1> tensorRange = { { sizeDim1 } };
	Tensor<DataType, 1, DataLayout, IndexType> in(tensorRange);
	Tensor<DataType, 1, DataLayout, IndexType> in1(tensorRange);
	memset(in1.data(), 1, in1.size() * sizeof(DataType));
	DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.size() * sizeof(DataType)));
	sycl_device.memset(gpu_in_data, 1, in.size() * sizeof(DataType));
	sycl_device.memcpyDeviceToHost(in.data(), gpu_in_data, in.size() * sizeof(DataType));
	for (IndexType i = 0; i < in.size(); i++) {
		VERIFY_IS_EQUAL(in(i), in1(i));
	}
	sycl_device.deallocate(gpu_in_data);
}

template<typename DataType, int DataLayout, typename IndexType>
void
test_device_exceptions(const Eigen::SyclDevice& sycl_device)
{
	VERIFY(sycl_device.ok());
	IndexType sizeDim1 = 100;
	array<IndexType, 1> tensorDims = { { sizeDim1 } };
	DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(sizeDim1 * sizeof(DataType)));
	sycl_device.memset(gpu_data, 1, sizeDim1 * sizeof(DataType));

	TensorMap<Tensor<DataType, 1, DataLayout, IndexType>> in(gpu_data, tensorDims);
	TensorMap<Tensor<DataType, 1, DataLayout, IndexType>> out(gpu_data, tensorDims);
	out.device(sycl_device) = in / in.constant(0);

	sycl_device.synchronize();
	VERIFY(!sycl_device.ok());
	sycl_device.deallocate(gpu_data);
}

template<typename DataType>
void
sycl_device_test_per_device(const cl::sycl::device& d)
{
	std::cout << "Running on " << d.template get_info<cl::sycl::info::device::name>() << std::endl;
	QueueInterface queueInterface(d);
	auto sycl_device = Eigen::SyclDevice(&queueInterface);
	test_device_memory<DataType, RowMajor, int64_t>(sycl_device);
	test_device_memory<DataType, ColMajor, int64_t>(sycl_device);
	/// this test throw an exception. enable it if you want to see the exception
	// test_device_exceptions<DataType, RowMajor>(sycl_device);
	/// this test throw an exception. enable it if you want to see the exception
	// test_device_exceptions<DataType, ColMajor>(sycl_device);
}

EIGEN_DECLARE_TEST(cxx11_tensor_device_sycl)
{
	for (const auto& device : Eigen::get_sycl_supported_devices()) {
		CALL_SUBTEST(sycl_device_test_per_device<float>(device));
	}
}
